What indicates statistical significance in research findings?

Statistical significance in research findings is primarily indicated by a small probability of obtaining results by chance. When researchers conduct hypothesis tests, they typically use a significance level (commonly set at 0.05). If the p-value obtained from the statistical analysis is less than this threshold, it suggests that the observed results are unlikely to have occurred if the null hypothesis were true. Therefore, a small p-value implies that there is a meaningful effect or relationship present rather than one that is due to random chance. This is fundamental in distinguishing genuine findings from those that might arise from variability in data.

The other options may relate to aspects of study design and reliability, but they do not directly define statistical significance. For instance, while a large sample size can increase the power of a study and help to detect effects, it does not guarantee that results are statistically significant on its own. Similarly, high experimental control contributes to the validity of the findings, but it doesn’t specify the likelihood of results occurring purely by chance. Lastly, a direct correlation between variables indicates a relationship but does not confirm that this relationship is statistically significant without further statistical testing.